Abstract-Data mining is the useful tool to discovering the knowledge from large data. Different methods & algorithms are available in data mining. Classification is most common method used for finding the mine rule from the large database. Decision tree method generally used for the Classification, because it is the simple hierarchical structure for the user understanding & decision making. Various data mining algorithms available for classification based on Artificial Neural Network, Nearest Neighbour Rule & Baysen classifiers but decision tree mining is simple one. ID3 and C4.5 algorithms have been introduced by J.R Quinlan which produce reasonable decision trees. The objective of this paper is to present these algorithms. At first we present the classical algorithm that is ID3, then highlights of this study we will discuss in more detail C4.5 this one is a natural extension of the ID3 algorithm. And we will make a comparison between these two algorithms and others algorithms such as C5.0 and CART.
Diabetic retinopathy, the most common diabetic eye disease, occurs when blood vessels in the retina change. Sometimes these vessels swell and leak fluid or even close off completely. In other cases, abnormal new blood vessels grow on the surface of the retina. Early detection can potentially reduce the risk of blindness. This paper presents an automated method for the detection of exudates in retinal color fundus images with high accuracy, First, the image is converted to HSI model, after preprocessing possible regions containing exudate, the segmented image without Optic Disc (OD) using algorithm Graph cuts, Invariant moments Hu in extraction feature vector are then classified as exudates and non-exudates using a Neural Network Classifier. All tests are applied on database DIARETDB1.
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